Explaining visual classifiers with saliency maps has allowed researchers to investigate the localization abilities implicitly learned by these models.
Some works have also used explanations of visual classifiers for weakly-supervised object localization [1]}, [2]}.
In object detection, however, the localization decisions of the model are explicit as they are expressed directly in the outputs of the model.
Therefore, one might assume that exploring spatial importance in this case is redundant, that the model has already predicted bounding boxes around everything it deems important. In our experiments with D-RISE, we observe that DNN models also learn to utilize contextual regions to detect objects. For instance the last column in Fig. REF  shows how the tap helps to localize the sink even when it is clearly outside the detected box. In fact, the importance of contextual information for object detection has long been established for both humans [3]}, [4]} and machines [5]}, [6]}. Another reason for studying object detector's saliency is that not all sub-regions within the object's bounding box are equally important.
Some object parts are more discriminant, while others may occur with objects of different categories, e.g., cat faces are highlighted as more important by the network than its body (third column in Fig. REF ).
